Deep Transfer Learning Based PPI Prediction for Protein Complex Detection

Yuan, Xin; Deng, Hangyu; Hu, Jinglu

doi:10.1109/smc52423.2021.9658656

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…It diminishes the need for conventional biomedical natural language processing tools like the dependency parser. Yuan et al [43] introduced a predictor for PPI prediction, using deep transfer learning. They developed a deep PPI detector to forecast unknown PPIs, thereby completing the known PPI network.…”

Section: Convolutional Neural Network (Cnn) Techniquementioning

confidence: 99%

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Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Taha

2023

Preprint

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This survey paper provides an in-depth analysis of various machine learning techniques and algorithms that are utilized in the detection of PPI (Protein-Protein Interactions). For every technique examined, the paper evaluates its efficiency, shortcomings, possibilities for enhancement, and outlook for the future. A major challenge in current survey papers focusing on machine learning algorithms for PPI identification is the successful categorization of these algorithms. To overcome this challenge, the paper introduces a novel hierarchical taxonomy that organizes algorithms into more intricate categories and distinct techniques. The proposed taxonomy is constructed on a four-tier structure, beginning with the broad methodology category, and ending with specific sub-techniques. This structure facilitates a more systematic and exhaustive categorization of algorithms, aiding researchers in grasping the connections between different algorithms and techniques. Included in the paper are both empirical and experimental assessments to classify the various techniques. The empirical assessment judges the techniques according to four standards. The experimental evaluations carry out the following rankings: (1) the algorithms that employ the same specific sub-technique, (2) the different sub-techniques that employ the same technique, (3) the different techniques that employ the same methodology sub-category, and (4) the different methodology sub-categories within the same methodology category. By merging the new methodological taxonomy, empirical analyses, and experimental evaluations, the paper provides a multifaceted and thorough comprehension of the machine learning methods and algorithms for PPI detection. This synthesis helps researchers make well-informed decisions. In its conclusion, the paper furnishes crucial insights into the future possibilities of machine learning techniques for PPI identification, underscoring potential advancements and areas ripe for continued exploration and development

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Section: Convolutional Neural Network (Cnn) Techniquementioning

confidence: 99%

“…(CNN) [41][42][43][44] The technique uses a filter or kernel to slide over an input matrix (e.g., protein sequence) to form a feature map, preserving pixel relationships by detecting image attributes with small input data portions. After each convolution, a nonlinear layer like ReLU introduces nonlinear aspects.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Taha

2023

Preprint

View full text Add to dashboard Cite

show abstract

Deep Transfer Learning Based PPI Prediction for Protein Complex Detection

Cited by 1 publication

References 13 publications

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

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